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REHE: Fast variance components estimation for linear mixed models.

Kun Yue1, Jing Ma2, Timothy Thornton1

  • 1Department of Biostatistics, University of Washington, Seattle, Washington, USA.

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|October 18, 2021
PubMed
Summary
This summary is machine-generated.

New restricted Haseman-Elston (REHE) regression methods offer fast, robust variance component estimation for linear mixed models in genetic studies. These methods provide accurate, non-negative estimates, outperforming standard techniques in large or small sample scenarios.

Keywords:
genome-wide association studyheritability studylinear mixed modelrestricted Haseman-Elston regressionvariance component

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Area of Science:

  • Genetics
  • Statistical Modeling
  • Bioinformatics

Background:

  • Linear mixed models (LMMs) are essential in ecological and biological research, particularly for genetic studies.
  • Accurate estimation of variance components is critical for LMMs.
  • Existing methods like REML are computationally intensive for large datasets and unstable for small ones, while HE regression can produce negative variance estimates.

Purpose of the Study:

  • To develop computationally efficient and statistically robust alternatives for variance component estimation in LMMs.
  • To address the limitations of existing methods, such as REML's inefficiency and HE's potential for negative variance estimates.
  • To introduce non-negative variance estimates with comparable accuracy to REML.

Main Methods:

  • Proposed restricted Haseman-Elston (REHE) regression and a resampling-based variant (reREHE).
  • Developed an inference framework for the REHE estimator.
  • Employed regularized estimation strategies to enhance stability and accuracy.

Main Results:

  • REHE and reREHE provide fast and robust alternatives for variance component estimation.
  • The proposed methods yield non-negative variance estimates.
  • REHE demonstrated comparable accuracy to REML in benchmark simulation studies and real data analysis.

Conclusions:

  • REHE and reREHE offer significant improvements over standard methods for variance component estimation in LMMs.
  • These new methods are particularly advantageous for large sample sizes and situations requiring non-negative variance estimates.
  • The REHE framework provides a reliable and efficient tool for genetic and biological data analysis.